Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study

Computers in Biology and Medicine

Volumn 40, Issue 8, 2010, Pages 705-714

  Lima, Clodoaldo A M ^a   Coelho, André L V ^b   Eisencraft, Marcio ^c  

^a MACKENZIE PRESBYTERIAN UNIVERSITY   (Brazil)

^b UNIVERSITY OF FORTALEZA   (Brazil)

^c UNIVERSIDADE FEDERAL DO ABC   (Brazil)

Author keywords

EEG signal classification; Epilepsy; Kernel functions; Least squares support vector machines; Sensitivity analysis

Indexed keywords

BIOLOGICAL SIGNALS; COMMUNICATION CHANNEL; DATA SETS; EEG SIGNAL CLASSIFICATION; EEG SIGNALS; ELECTRICAL ACTIVITIES; ELECTROENCEPHALOGRAM SIGNALS; HUMAN BRAIN; KERNEL FUNCTION; KERNEL FUNCTIONS; KERNEL MACHINE; LEAST SQUARE; LEAST SQUARES SUPPORT VECTOR MACHINES; NEUROLOGICAL DISORDERS; PARAMETER VALUES; PERFORMANCE LEVEL; SENSITIVITY PROFILES; SVM CLASSIFIERS; SVM MODEL;

BRAIN; BRAIN COMPUTER INTERFACE; ELECTROENCEPHALOGRAPHY; INTERFACES (COMPUTER); OPTICAL SENSORS; SUPPORT VECTOR MACHINES; VECTORS;

SENSITIVITY ANALYSIS;

ARTICLE; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; PERFORMANCE; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; LEAST-SQUARES ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77955580523     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2010.06.005     Document Type: Article

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